U.S. patent application number 10/600044 was filed with the patent office on 2004-12-23 for system and method for adaptive medical image registration.
This patent application is currently assigned to Confirma, Inc.. Invention is credited to Niemeyer, Tanya L., Wood, Chris H..
Application Number | 20040260170 10/600044 |
Document ID | / |
Family ID | 33517643 |
Filed Date | 2004-12-23 |
United States Patent
Application |
20040260170 |
Kind Code |
A1 |
Wood, Chris H. ; et
al. |
December 23, 2004 |
System and method for adaptive medical image registration
Abstract
In one embodiment, an adaptive medical image registration
procedure includes a motion estimation procedure involving
estimating or determining an amount of patient or tissue motion
along a set of axes; an evaluation procedure involving evaluating
an estimated amount of motion relative to a correction threshold;
and a correction procedure involving performing a two dimensional
image resampling, a three dimensional image resampling, or possibly
avoiding an image resampling based upon a relationship between an
estimated amount of motion and the correction threshold. Axes
considered by a motion estimation procedure may include an axis of
lowest image resolution, and the correction threshold may have a
value given by a fraction of a lowest image resolution.
Inventors: |
Wood, Chris H.; (North Bend,
WA) ; Niemeyer, Tanya L.; (Seattle, WA) |
Correspondence
Address: |
DAVIS WRIGHT TREMAINE, LLP
2600 CENTURY SQUARE
1501 FOURTH AVENUE
SEATTLE
WA
98101-1688
US
|
Assignee: |
Confirma, Inc.
Kirkland
WA
|
Family ID: |
33517643 |
Appl. No.: |
10/600044 |
Filed: |
June 20, 2003 |
Current U.S.
Class: |
600/410 |
Current CPC
Class: |
G06T 11/005 20130101;
G06T 7/20 20130101; A61B 6/032 20130101; G06T 7/0016 20130101; G06T
2207/30004 20130101; A61B 6/5264 20130101; G01R 33/563 20130101;
G06T 2211/412 20130101; G06T 7/30 20170101; A61B 6/481
20130101 |
Class at
Publication: |
600/410 |
International
Class: |
A61B 005/05 |
Claims
I/we claim:
1. A method for adaptive registration of a set of medical images
corresponding to a patient, comprising: estimating an amount of
patient motion corresponding to the set of medical images; and
selectively performing an image resampling in accordance with an
estimated amount of patient motion.
2. The method of claim 1, wherein estimating the amount of patient
motion comprises performing a motion estimation procedure that
involves patient motion in a directional axis corresponding to a
lowest image resolution.
3. The method of claim 2, wherein the axis corresponds to an MRI
slice thickness.
4. The method of claim 1, wherein estimating the amount of patient
motion comprises performing one estimated motion procedure selected
from the group of a two dimensional and a three dimensional motion
estimation procedure in accordance with one motion model selected
from the group of a rigid and a nonrigid motion model.
5. The method of claim 1, wherein selectively performing an image
resampling comprises: performing a comparison between the estimated
amount of patient motion and a correction threshold; and performing
the image resampling based upon the comparison.
6. The method of claim 5, wherein the correction threshold
corresponds to a fraction of an image resolution.
7. The method of claim 6, wherein the fraction has a value
approximately between 0.4 and 0.8.
8. The method of claim 6, wherein the fraction has a value of
approximately 0.5.
9. The method of claim 5, wherein the correction threshold
corresponds to a fraction of an image resolution along a lowest
image resolution axis.
10. The method of claim 9, wherein the axis corresponds to an MRI
image slice thickness.
11. The method of claim 16, wherein the set of medical images
comprises a set of imaging signals, wherein an imaging signal may
be characterized relative to a background imaging signal intensity,
a precontrast imaging signal intensity corresponding to a lesion,
and a postcontrast imaging signal intensity corresponding to the
lesion, and wherein the fraction has a value that depends upon at
least one from the group of a background imaging signal intensity,
a precontrast imaging signal intensity, and a postcontrast imaging
signal intensity.
12. The method of claim 5, wherein performing the image resampling
comprises performing a first image resampling procedure in the
event that the estimated amount of patient motion equals or exceeds
the correction threshold and performing a second image resampling
procedure in the event that the estimated amount of patient motion
is less than the correction threshold.
13. The method of claim 5, wherein performing the image resampling
comprises performing a three dimensional image resampling procedure
in the event that the estimated amount of patient motion equals or
exceeds the correction threshold.
14. The method of claim 5, wherein performing the image resampling
comprises performing a two dimensional image resampling procedure
in the event that the estimated amount of patient motion is less
than the correction threshold.
15. A method for adaptive registration of a set of medical images
corresponding to a patient, comprising: estimating an amount of
patient motion corresponding to the set of medical images;
performing a comparison between the estimated amount of patient
motion and a correction threshold; performing a three dimensional
image resampling procedure in the event that the estimated amount
of patient motion equals or exceeds the correction threshold; and
performing a two dimensional image resampling procedure in the
event that the estimated amount of patient motion is less than the
correction threshold.
16. The method of claim 15, wherein the correction threshold
corresponds to a fraction of an image resolution.
17. The method of claim 16, wherein the fraction has a value
between approximately 0.4 and 0.8.
18. The method of claim 16, wherein the fraction has a value of
approximately 0.5.
19. The method of claim 15, wherein the correction threshold
corresponds to a fraction of an image resolution along a lowest
image resolution axis.
20. The method of claim 19, wherein the axis corresponds to an MRI
image slice thickness.
21. The method of claim 15, further comprising the step of avoiding
an image resampling in the event that the estimated amount of
patient motion is less than the correction threshold by a
predetermined amount.
22. A method for adaptive registration of a set of medical images
corresponding to a patient, comprising: estimating an amount of
patient motion corresponding to the set of medical images;
performing a comparison between the estimated amount of patient
motion and a correction threshold; performing an image resampling
procedure in the event that the estimated amount of patient motion
equals or exceeds the correction threshold; and avoiding an image
resampling in the event that the estimated amount of patient motion
is less than the correction threshold.
23. The method of claim 22, wherein the correction threshold
corresponds to a fraction of an image resolution.
24. The method of claim 23, wherein the fraction has a value
between approximately 0.4 and 0.8.
25. The method of claim 23, wherein the fraction has a value of
approximately 0.5.
26. The method of claim 22, wherein the correction threshold
corresponds to a fraction of an image resolution along a lowest
image resolution axis.
27. The method of claim 26, wherein the axis corresponds to an MRI
image slice thickness.
28. The method of claim 23, wherein the set of medical images
comprises a set of imaging signals, wherein an imaging signal may
be characterized relative to a background imaging signal intensity,
a precontrast imaging signal intensity corresponding to the lesion,
and a postcontrast imaging signal intensity corresponding to a
lesion, and wherein the fraction has a value that depends upon at
least one from the group of a background imaging signal intensity,
a precontrast imaging signal intensity, and a postcontrast imaging
signal intensity.
29. A system for adaptive registration of a set of medical images
corresponding to a patient, comprising: a processing unit; and a
computer readable medium containing program instructions to cause
the processing unit to perform a comparison between an estimated
amount of patient motion and a correction threshold; and select one
from the group of performing a first image resampling procedure,
performing a second image resampling procedure, and avoiding an
image resampling in accordance with a relationship between the
estimated amount of patient motion and the correction
threshold.
30. The system of claim 29, wherein the correction threshold
corresponds to a fraction of an image resolution.
31. The system of claim 30, wherein the fraction has a value
between approximately 0.4 and 0.8.
32. The system of claim 30, wherein the correction fraction has a
value of approximately 0.5.
33. The system of claim 29, wherein the correction threshold
corresponds to a fraction of an image resolution along a lowest
image resolution axis.
34. The system of claim 33, wherein the axis corresponds to an MRI
image slice thickness.
35. The system of claim 29, wherein selecting performing the first
image resampling procedure comprises performing a three dimensional
image resampling in the event that the estimated amount of patient
motion equals or exceeds the correction threshold.
36. The system of claim 29, wherein selecting performing the second
image resampling procedure comprises performing a two dimensional
image resampling in the event that the estimated amount of patient
motion is less than the correction threshold.
37. The system of claim 29, wherein selecting avoiding an image
resampling comprises avoiding an image resampling in the event that
the estimated amount of patient motion is less than the correction
threshold by a predetermined amount.
38. The system of claim 29, further comprising: a medical imaging
system; and a data storage device.
39. The system of claim 38, wherein the medical imaging system
comprises an MRI system.
40. The system of claim 39, wherein the medical imaging system
comprises a breast MRI system.
41. A computer readable medium storing program instructions to
cause a processor to: estimate an amount of patient motion
corresponding to a set of medical images; and select one from the
group of performing a first image resampling procedure, performing
a second image resampling procedure, and avoiding an image
resampling in accordance with a relationship between an estimated
amount of patient motion and the correction threshold.
42. The computer readable medium of claim 41, wherein the
correction threshold comprises a fraction of an image
resolution.
43. The computer readable medium of claim 41, wherein the fraction
has a value between approximately 0.4 and 0.8.
44. The computer readable medium of claim 41, wherein the fraction
has a value of approximately 0.5.
45. The computer readable medium of claim 41, wherein the
correction threshold corresponds to a fraction of an image
resolution along a lowest image resolution axis.
46. The method of claim 45, wherein the axis corresponds to an MRI
image slice thickness.
47. The computer readable medium of claim 41, wherein selecting
performing the first image resampling procedure comprises
performing a three dimensional image resampling in the event that
the estimated amount of patient motion equals or exceeds the
correction threshold.
48. The computer readable medium of claim 41, wherein selecting
performing the second image resampling procedure comprises
performing a two dimensional image resampling in the event that the
estimated amount of patient motion is less than the correction
threshold.
49. The computer readable medium of claim 41, wherein selecting
avoiding the image resampling comprises avoiding the image
resampling in the event that the estimated amount of patient motion
is significantly less than the correction threshold by a
predetermined amount.
Description
TECHNICAL FIELD
[0001] The present disclosure relates generally to medical imaging
techniques. More particularly, the present disclosure describes
systems and methods for adaptively registering medical images in
accordance with relationships between particular parameters, for
example, patient movement and spatial resolution.
BACKGROUND
[0002] Medical imaging technologies can provide detailed
information useful for differentiating, diagnosing, or monitoring
the condition, structure, and/or extent of various types of tissue
within a patient's body. In general, medical imaging technologies
detect and record manners in which tissues respond in the presence
of applied signals and/or injected or ingested substances, and
generate visual representations indicative of such responses.
[0003] A variety of medical imaging technologies exist, including
Computed Tomography (CT), Positron Emission Tomography (PET),
Single Photon Emission Computed Tomography (SPECT), and Magnetic
Resonance Imaging (MRI). Any given medical imaging technology may
be particularly well suited for differentiating between specific
types of tissues. A contrast agent administered to the patient may
selectively enhance or affect the imaging properties of particular
tissue types to facilitate improved tissue differentiation. For
example, MRI may excel at distinguishing between various types of
soft tissue, such as malignant and/or benign breast tumors or
lesions that are contrast enhanced relative to healthy breast
tissue in the presence of Gadolinium DPTA or another contrast
agent.
[0004] Particular imaging techniques, for example, certain MRI
techniques, may scan a volume of tissue within an anatomical region
of interest. Scan data corresponding to an anatomical volume under
consideration may be transformed into or reconstructed as a series
of planar images or image "slices." For example, data generated
during a breast MRI scan may be reconstructed as a set of 40 or
more individual image slices. Any given image slice comprises an
array of volume elements or voxels, where each voxel corresponds to
an imaging signal intensity within an incremental volume that may
be defined in accordance with x, y, and z axes or dimensions. The z
axis commonly corresponds to a distance increment between image
slices, that is, image slice thickness.
[0005] Medical imaging techniques may generate or obtain imaging
data corresponding to a given anatomical region at different times
or sequentially through time to facilitate detection of changes
within the anatomical region from one scan series to another.
Temporally varying, tissue dependent contrast agent uptake
properties may facilitate accurate identification of particular
tissue types. For example, in breast tissue, healthy or normal
tissue exhibits different contrast agent uptake behavior over time
than tumorous tissue. Moreover, malignant lesions exhibit different
contrast agent uptake behavior than benign lesions ("Measurement
and visualization of physiological parameters in contrast-enhanced
breast magnetic resonance imaging," Paul A. Armitage et al.,
Medical Imaging Understanding and Analysis, July 2001, University
of Birmingham).
[0006] In view of the foregoing, comparisons between 1) an image
obtained prior to contrast agent administration (i.e., a
"pre-contrast image") and one or more corresponding images obtained
following contrast agent administration (i.e., "post-contrast
images"); and/or 2) a temporal sequence of post-contrast images
relative to each other may serve to highlight differences between
and/or within tissues, thereby aiding medical diagnostic
procedures.
[0007] Medical images can be characterized by their spatial
resolution. As previously indicated, an MRI slice comprises a set
of volume elements or voxels, where each voxel corresponds to a
signal intensity or value for a quantized tissue volume. An
exemplary MRI slice may have a resolution of 256.times.256 voxels
with respect to x and y reference directions or axes, where each
voxel represents imaging data for a 1.0.times.1.0.times.2.5
mm.sup.3 tissue volume relative to x, y, and z axes,
respectively.
[0008] Successful detection, characterization, and/or
identification of tissue boundaries and/or small tissue structures
such as newly or recently developed lesions or tissue abnormalities
requires the ability to identify tissue boundaries and/or indicate
temporal tissue changes at the level of fractional voxels,
individual voxels, and/or very small voxel groups. If a patient
moves even slightly during or between image acquisition procedures,
the imaged shape, size, and/or relative location of a given tissue
boundary or structure may be distorted or shifted relative to its
actual shape, size, and/or location. Unfortunately, some patient
movement will essentially always exist. Patient movement may arise
from several sources, including changes in patient relaxation or
tension levels over time, for example, prior to, during, and
following injection of a contrast agent; minor positional
adjustments; and respiration. Patient movement can be particularly
problematic when imaging nonrigid or readily deformable anatomical
structures such as breasts.
[0009] To reduce the effects of patient motion upon imaging
accuracy, medical imaging techniques may include registration
correction procedures. Current registration correction procedures
involve selection of a reference image from within an image series;
generation or determination of motion estimation parameters; and
motion correction of acquired images with respect to the reference
image. The motion correction involves image resampling with
subvoxel accuracy. Such resampling may occur, for example, through
an interpolation procedure. Unfortunately, image resampling itself
can degrade or deteriorate the spatial resolution of imaging
information. Such degradation can be dependent upon one or more
aspects of the registration correction procedure itself. A need
exists for a system and method that situationally consider the
potential impact that registration correction may have upon imaging
accuracy.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 is a side view schematic illustration of an exemplary
precontrast image slice in which a lesion has been imaged within
spatial boundaries corresponding to a first voxel.
[0011] FIG. 2 is a side view schematic illustration of a first and
a second exemplary postcontrast image slice in which a lesion has
been imaged across a voxel belonging to the first postcontrast
slice and a voxel belonging to the second postcontrast slice as a
result of patient or tissue motion.
[0012] FIG. 3 is a graph relating a fractional normalized tissue
displacement to an uncorrected and a corrected postcontrast signal
enhancement percentage when precontrast imaging signals are less
than or generally less than background imaging signals.
[0013] FIG. 4 is a graph relating a fractional normalized tissue
displacement to an uncorrected and a corrected postcontrast signal
enhancement percentage when precontrast and background imaging
signals are equal or essentially equal.
[0014] FIG. 5 is a graph relating a fractional normalized tissue
displacement to an uncorrected and a corrected postcontrast signal
enhancement percentage when precontrast imaging signals are greater
than or generally greater than background imaging signals.
[0015] FIG. 6 is a flowchart of a procedure for adaptive
registration of medical images according to an embodiment of the
invention.
[0016] FIG. 7 is a flowchart of exemplary evaluation and selective
correction procedures according to an embodiment of the
invention.
[0017] FIG. 8 is a flowchart of exemplary evaluation and selective
correction procedures according to another embodiment of the
invention.
[0018] FIG. 9 is a block diagram of a system for adaptive
registration of medical images according to an embodiment of the
invention.
DETAILED DESCRIPTION
[0019] The present disclosure describes systems and/or methods for
adaptive registration of medical images. Depending upon embodiment
details, adaptive medical image registration may be based upon
relationships between various imaging parameters and/or results
obtained from image analysis. Such parameters and/or results may
include image resolution in one or more dimensions; an amount of
patient or tissue movement in one or more dimensions; and/or
relative imaging signal intensity levels at one or more times for
particular categories of tissue. Portions of the following
description detail manners in which various embodiments of the
present invention may be applied in an MRI context, particularly
MRI imaging of breast tissue. Notwithstanding, various embodiments
of systems and/or methods in accordance with the present invention
may be applicable to essentially any type of medical imaging
technology and/or technique that utilizes a contrast agent.
[0020] In general, at any particular time, the intensity of an
imaging signal associated with any given voxel depends upon the
types of tissues within an anatomical region corresponding to the
voxel; the presence or absence of a contrast agent in such tissues;
and the temporal manners in which such tissues respond following
contrast agent administration. In several types of breast MRI
situations, normal or healthy tissue exhibits a background signal
intensity in the absence of a contrast agent, while abnormal or
tumorous tissue exhibits a low or reduced signal intensity relative
to the background intensity. Prior to contrast agent
administration, abnormal tissue therefore typically appears darker
than normal tissue. In the presence of a contrast agent, lesions or
certain types of abnormal tissue typically exhibit an enhanced or
increased signal intensity relative to the background intensity. In
certain breast MRI situations, MRI situations involving other
anatomical regions, and/or imaging applications involving other
imaging technologies, relationships between background,
precontrast, and/or postcontrast signal intensity may differ, in
manners understood by those skilled in the art.
[0021] On an individual voxel basis, the relative degree to which
an imaging signal corresponding to a lesion or abnormal or
undesirable tissue is enhanced at any given time following contrast
agent administration may be defined as a signal enhancement
percentage that is normalized to a lowest signal intensity within a
voxel. Commonly, this lowest signal intensity is either the
background signal intensity or the abnormal tissue's precontrast
signal intensity.
[0022] Patient or tissue movement or motion may cause an imaging
signal corresponding to a lesion within any particular voxel to be
displaced and/or distorted into a set of adjacent, adjoining,
and/or proximate voxels in any given direction or dimension. When
tissue movement occurs during acquisition of a given single slice,
imaging signal distortion affects voxels within the plane of that
slice. When tissue movement continues or occurs between one slice
acquisition and another, imaging signal displacement and/or
distortion can affect voxels in different slices. Following tissue
motion, the extent to which a lesion is imaged in an adjacent,
adjoining, or proximate voxel relative to voxel resolution may
affect signal enhancement percentages for the voxels involved, as
further detailed hereafter.
[0023] FIG. 1 is a schematic illustration of an exemplary
precontrast image slice 100 in which a lesion 150 has been imaged
within spatial boundaries corresponding to a first precontrast
voxel 110. In the precontrast slice 100, the lesion 150 may be
imaged as having a precontrast signal intensity (shown in dark
gray) that is less or lower than a background signal intensity
(shown in light gray). Following data or signal corresponding to
acquisition of a set or series of precontrast image slices that
includes the exemplary precontrast slice 100, a contrast agent may
be administered. After contrast agent administration, image
acquisition corresponding to a set or series of postcontrast slices
may occur. Relative to breast MRI, contrast agent uptake within a
lesion may provide a peak postcontrast imaging signal intensity
approximately 60 to 90 seconds after contrast agent administration.
Patient movement during or after precontrast imaging may affect how
the lesion 150 is imaged in one or more postcontrast slices.
[0024] FIG. 2 is a schematic illustration of a first 200 and a
second 202 exemplary postcontrast image slice, in which the lesion
150 of FIG. 1 has been imaged as spanning a portion of a first
postcontrast voxel 210 within the first postcontrast slice 200 and
a portion of a second postcontrast voxel 212 within the second
postcontrast slice 202 as a result of patient or tissue motion. In
the postcontrast slices 200, 202, the lesion 150 may be imaged as
having a postcontrast intensity (shown in white) that is greater or
higher than its precontrast intensity.
[0025] As a result of patient or tissue motion, the lesion 150 has
been imaged within spatial locations corresponding to two voxels
210, 212 across separate slices 200, 202 rather than within a
spatial extent corresponding to a single voxel 110 within a single
slice 100. Such motion has therefore caused a partial volume
artifact or imaging error. In response to this or a similar type of
volume artifact or imaging error, the present invention in one
embodiment may initiate or perform a registration correction
procedure in a selective or adaptive manner.
[0026] In the absence of any type of registration correction, an
uncorrected signal enhancement percentage corresponding to the
first postcontrast voxel 210 may be given by
% E.sub.210 u=((1-.alpha.)*POST+.alpha.*BG)-PRE)/PRE [1]
[0027] where BG corresponds to a background signal intensity; PRE
corresponds to a precontrast signal intensity associated with the
lesion 150; POST corresponds to a postcontrast signal intensity
associated with the lesion 150; and .alpha. may be defined as a
distance that the tissue of interest (i.e., the contrast enhanced
lesion) or an imaging signal corresponding thereto has shifted
along a particular axis or direction relative to a voxel resolution
along that axis or direction. In other words, .alpha. may represent
a resolution normalized fractional shift of contrast enhanced
tissue, which corresponds to a resolution normalized amount of
patient motion. The value of .alpha. may be a measured, estimated,
approximated, and/or derived quantity based upon imaging
information and/or implementation details.
[0028] In a manner analogous to that for Equation [1], an
uncorrected signal enhancement percentage for the second
postcontrast voxel 212 may be given by
% E.sub.212 u=((.alpha.*POST+(1-.alpha.)*BG)-BG)/BG [2]
[0029] During or in association with registration correction, image
resampling may be performed in a variety of manners depending upon
implementation details. For example, image resampling may be
performed in accordance with a linear, a polynomial, a spline, or a
sinc based procedure, or in accordance with essentially any type of
resampling technique capable of providing subvoxel accuracy. These
registration processes are well know in the art and need not be
described in greater detail herein.
[0030] In accordance with an exemplary linear interpolation based
registration correction, a registration corrected signal
enhancement percentage for the first postcontrast voxel 210 may be
given by 1 % E 210 c = ( ( 1 - ) * POST + * BG ) - ( ( 1 - ) * PRE
+ * BG ) ( ( 1 - ) * PRE + * BG ) [ 3 ]
[0031] In like manner, a registration corrected signal enhancement
percentage for the second postcontrast voxel 212 may be given by 2
% E 212 c = ( * POST + ( 1 - ) * BG ) - ( * PRE + ( 1 - ) * BG ) (
* PRE + ( 1 - ) * BG ) [ 4 ]
[0032] Valuation of Equations [1] through [4] yields different
results depending upon the value of .alpha.. Thus, the degree to
which tissue is contrast enhanced depends upon patient motion
relative to voxel or image resolution. Furthermore, the numerical
behavior of Equations [1] through [4] depends upon relative
relationships between background, precontrast, and postcontrast
imaging signal intensities. A variety of useful imaging signal
intensity reference relationships may be defined, including (a)
precontrast signal intensity less than background signal intensity;
(b) equal or essentially equal background and precontrast signal
intensities; and (c) precontrast signal intensity greater than
background signal intensity. In many or most types of breast MR
imaging situations, precontrast signal intensity is typically less
than background signal intensity and thus reference relationship
(a) generally holds. The applicability of a particular signal
intensity reference relationship to a given medical imaging
situation may depend upon imaging technology and/or techniques
employed; tissue types under consideration; contrast agent type;
and/or other factors. Manners in which various imaging signal
intensity reference relationships may affect an imaging signal
enhancement percentage are considered in detail hereafter.
[0033] In imaging situations in which an imaging signal intensity
or value associated with a postcontrast lesion is expected to be
higher or greater than an intensity associated with a precontrast
lesion, accurate lesion identification may be aided when a signal
enhancement percentage is increased or maximized. Such imaging
situations typically include breast MRI. In certain embodiments,
the present invention may adaptively select, initiate, and/or
perform a registration correction procedure in a manner that
maximizes a likelihood of lesion enhancement.
[0034] FIG. 3 is a graph 300 relating a fractional normalized
tissue displacement a to an uncorrected postcontrast signal
enhancement percentage curve or line 310 and a corrected
postcontrast signal enhancement percentage curve or line 320 when
precontrast imaging signals corresponding to a lesion are less than
or generally less than background imaging signals. The curve 320
comprises two curve portions showing the percent enhancement from
voxel 1 and voxel 2, respectively, with the curve 320 showing only
the maximum value of the percent enhancement. The percent
enhancement from voxel 1 is shown on the left portion of the curve
320 for values of .alpha. less than approximately 0.5. For values
of .alpha. greater than 0.5., the portion of the curve 320 is due
to the percent enhancement. The uncorrected curve 310 is generated
based upon Equation [1], while the corrected curve 320 is based
upon Equations [3] and [4]. In FIG. 3, the values of BG, PRE, and
POST are respectively defined as 150, 100, and 200.
[0035] In FIG. 3, if .alpha. is approximately equal to 0.3, for
example, a postcontrast enhancement percentage corresponding to the
uncorrected curve 310 is higher or larger than that corresponding
to the corrected curve 320. In such an imaging situation, one
embodiment of the present invention may avoid or omit performing a
correction or image resampling in order to enhance or maximize
imaging accuracy, such that an imaging result more closely
represents or indicates actual lesion boundaries and/or processes
occurring therein. In the event that a is approximately equal to
0.8, for example, a corrected curve 320 provides a higher or larger
postcontrast enhancement percentage than an uncorrected curve 310,
and thus in one embodiment the present invention may perform a
correction or image resampling in order to increase, enhance, or
maximize imaging accuracy in such a situation.
[0036] More generally, below a transition value or a transition
range of .alpha., an uncorrected curve 310 may provide a higher or
larger enhancement percentage than a corrected curve 320, while the
corrected curve 320 may provide a higher enhancement percentage
than the uncorrected curve 310 above the transition value or
transition range of .alpha.. As shown in FIG. 3, a transition value
or transition range of .alpha. may be approximately between 0.6 and
0.8. The transition value or transition range of a may vary
depending upon imaging technology, clinical conditions, and/or
various embodiment details (possibly including a manner of
estimating or determining .alpha.). In imaging situations in which
maximization of sensitivity to postcontrast signal enhancement
percentage is desired and PRE is expected to be less than BG,
particular embodiments of the present invention may initiate or
perform a first type of correction, for example, a 2D correction,
when a measured, estimated, approximated, or derived value of a is
below a certain transition value or falls within a first range; and
initiate or perform a second type of correction, for example, a 3D
correction, when a value of a is above such a transition value or
falls within a second range.
[0037] FIG. 4 is a graph 400 relating a fractional normalized
tissue displacement .alpha. to an uncorrected postcontrast signal
enhancement percentage curve or line 410 and a corrected
postcontrast signal enhancement percentage curve or line 420 when
precontrast and background imaging signals are equal or essentially
equal. The curve 420 comprises two curve portions showing the
percent enhancement from voxel 1 and voxel 2, respectively, with
the curve 420 showing only the maximum value of the percent
enhancement. In FIG. 4, the values of BG and PRE are defined as
100, and the value of POST is defined as 200. In a manner similar
to that described above with reference to FIG. 3, a transition
value for .alpha. may approximately equal 0.5, and/or a transition
range for .alpha. may approximately be between 0.45 and 0.55, under
conditions corresponding or generally corresponding to FIG. 4.
Thus, imaging accuracy may be enhanced or maximized in certain
embodiments by performing a first type of correction or avoiding a
correction when .alpha. is less than approximately 0.5; and
performing a second type of correction when .alpha. is greater than
approximately 0.5. As indicated in FIG. 4, a correction may be
unnecessary, avoided, or omitted when .alpha. is less than
approximately 0.5 because imaging accuracy is unaffected or
generally unaffected in such a situation. That is, the equations
defining the uncorrected postcontrast curve 410 and the corrected
postcontrast curve 420 generate identical or essentially identical
results when .alpha. is less than approximately 0.5, and thus
correction may be avoided. Avoidance of a correction when .alpha.
is less than approximately 0.5 may eliminate unnecessary
computation and save time.
[0038] FIG. 5 is a graph 500 relating a fractional normalized
tissue displacement a to an uncorrected postcontrast signal
enhancement percentage curve or line 510 and a corrected
postcontrast signal enhancement percentage curve or line 520 when
precontrast imaging signals are greater than or generally greater
than background imaging signals. As discussed above with respect to
FIGS. 3-4, the curve 520 comprises two curve portions showing the
percent enhancement from voxel 1 and voxel 2, respectively, with
the curve 520 showing only the maximum percent enhancement. In FIG.
5, the values of BG, PRE, and POST are respectively defined as 100,
150, and 200. As shown in FIG. 5, the corrected curve 520 enhances,
increases, or maximizes imaging accuracy relative to the
uncorrected curve 510 under such circumstances. Thus, in one
embodiment, the present invention may perform a correction when PRE
is greater than BG independent of a value of a; or possibly
determine a different type of resampling procedure that may give
rise to a transition value or transition region for a when PRE is
greater than BG, and selectively initiate or perform a correction
in accordance therewith.
[0039] The foregoing examples considered an effect of patient
motion relative to resolution along a single axis or dimension.
Certain embodiments may consider patient or tissue motion along an
axis that corresponds to a lowest image resolution. In MRI
situations, an axis of lowest resolution typically corresponds to
image slice thickness, and is commonly defined as a z axis. In
general, various embodiments of systems and/or methods in
accordance with the present invention may adaptively consider
resolution normalized fractional shifts (i.e., .alpha.) and/or
mathematical equivalents thereto and/or analogs thereof along or in
multiple dimensions, including a dimension of lowest resolution.
Depending upon embodiment details, systems and methods in
accordance with the present invention that consider an
.alpha..sub.x, an .alpha..sub.y, and/or an .alpha..sub.z and/or one
or more mathematical equivalents thereto and/or analogs thereof may
adaptively select between performing no correction, a two
dimensional (2D) correction, and/or a three dimensional (3D)
correction.
[0040] FIG. 6 is a flowchart of a procedure 600 for adaptive
registration of medical images according to an embodiment of the
invention. In one embodiment, the adaptive registration procedure
600 includes an acquisition procedure 602 that involves acquiring,
generating, retrieving, receiving, and/or obtaining imaging data
corresponding to a set or series of medical images. In one
embodiment, the acquisition procedure 602 involves or is directed
toward precontrast and/or postcontrast image slices, which may
correspond to breast images or other types of MR images.
[0041] The adaptive registration procedure 600 may further include
a tiling procedure 604 involving determination of whether
registration should consider a local subset of imaging data or
global imaging data; and identification or specification of one or
more local subset parameters if applicable. Relative to breast MRI,
a tiling procedure 604 may determine whether to perform
registration in accordance with a window or subset of imaging
information associated with a plurality of image slices. For
example, a tiling procedure 604 may determine that registration
corresponding to precontrast and/or postcontrast imaging data for a
left breast is appropriate or required, and ignore imaging data for
a right breast. Depending upon embodiment details, one or more
portions of a tiling procedure 604 may involve manual input and/or
an automated procedure.
[0042] The adaptive registration procedure 600 may additionally
include a motion estimation procedure 606, which involves
estimating, approximating, or determining patient or tissue motion
based upon the imaging data under consideration. Motion estimation
may involve generating one or more motion vectors by determining
and/or optimizing a set of spatial transform parameters defined in
accordance with a motion model. The motion model may be capable of
accounting for various types of 2D or 3D motion or deformation of
rigid and/or nonrigid tissues.
[0043] In general, a number of motion estimation techniques
suitable for medical imaging and/or image processing may be
applicable to various embodiments of the present invention.
Descriptions of such motion estimation techniques may be found in
references such as (a) "Comparison and Evaluation of Retrospective
Intermodality Brain Image Registration Techniques," West et al.,
JCAT 1997; and (b) "A Survey of Medical Image Registration," Maintz
and Viergever, Medical Image Analysis, 1998.
[0044] In one embodiment, the motion estimation procedure 606 may
involve selection or identification of a motion model and/or a
motion estimation technique; selection or identification of a
reference image; and/or determination of one or more motion vectors
or parameters for a set of images relative to the reference image
along an axis or direction of lowest resolution (typically the axis
corresponding to slice thickness in MRI situations) as well as one
or more other axes. Selection of a motion model and/or a motion
estimation technique may involve manual input and/or one or more
automated procedures, possibly based upon clinical conditions such
as imaging technology type and configuration, tissue types under
consideration, image resolution, and/or other factors. In general,
motion estimation in accordance with a nonrigid motion model is
more computationally intensive than motion estimation in accordance
with a rigid motion model, and 3D motion estimation is more
computationally intensive than-2D motion estimation. Thus,
selection or identification of a motion model and/or a motion
estimation technique may additionally or alternatively be based
upon available computational resources or capabilities. An
alternate embodiment may rely upon a single type of motion model
and/or motion estimation technique.
[0045] In accordance with the motion estimation procedure 606,
determination of one or more motion vectors and/or motion
parameters may involve operations in a spatial (voxel) domain or a
spectral (frequency) domain. In one embodiment, a motion estimation
procedure 606 may involve a minimization of gray level or voxel
property based similarity measures in accordance with an
optimization procedure (for example, a simplex minimization, a
direction set, a conjugate gradient, or a simulated annealing
procedure). Gray level similarity measures may include a sum of
squared or absolute differences; a cross-correlation measure; an
intensity ratio variance; mutual information; and/or a
deterministic or stochastic sign change. A sum of squared
differences technique may be computationally efficient at the
possible expense of some accuracy, while a mutual information
technique may be highly accurate at the possible expense of some
computational speed. In an embodiment that performs motion
estimation in accordance with an affine transform, a least squares
technique rather than an optimization search may provide a direct
solution.
[0046] During the motion estimation procedure 606, images or
imaging data under consideration may be scaled or reduced in size
to generate an initial motion estimate; and then scaled or
increased to an original size to generate a final motion estimate,
which may provide increased robustness. For example, a motion
estimation procedure 604 directed toward images characterized by a
512.times.512 resolution may first estimate motion at a 64.times.64
resolution, then estimate motion at a 128.times.128 resolution,
subsequently estimate motion at a 256.times.256 resolution, and
finally estimate motion at a 512.times.512 resolution. Images or
imaging data under consideration may additionally or alternatively
be subdivided into smaller blocks for local motion estimation,
which may be useful for estimating nonrigid motion.
[0047] The adaptive registration procedure 600 may further include
an evaluation procedure 608 that involves comparing a result
generated or obtained by the motion estimation procedure 606 to a
correction threshold. In one embodiment, a motion estimation result
may comprise a motion vector that may specify or indicate an
estimated motion value along one or more axes, including an axis
corresponding to lowest image resolution. As indicated above, for
MRI image data, the axis of lowest resolution is typically an axis
corresponding to image slice thickness, and is commonly defined as
z. In one embodiment, a correction threshold may comprise a
resolution value corresponding to one or more axes, including a
lowest resolution axis, where each such resolution value is
multiplied by a corresponding fraction. Thus, a correction
threshold may specify or correspond to a fractional resolution
along one or more axes.
[0048] The evaluation procedure 608 may determine whether one or
more motion estimation results are greater or less than
corresponding correction thresholds. Alternatively or additionally,
the evaluation procedure 608 may determine whether one or more
motion estimation results fall within particular corresponding
correction ranges. Evaluation or comparison of a motion estimation
result relative to a fractional resolution value may be
mathematically equivalent or analogous to determining an .alpha.
value of a type described above. The value of a correction
threshold may be influenced by imaging technology; actual and/or
expected background, precontrast, and/or postcontrast signal
intensities; and/or other factors. One or more correction
thresholds may be stored in and/or retrieved from a memory such as
a lookup table based upon applicability to particular clinical
situations.
[0049] The adaptive registration procedure 600 may further include
a selective correction procedure 610 that involves selectively
initiating or performing an image resampling or correction in
accordance with a 2D or 3D rigid or nonrigid correction based upon
a result obtained by or in conjunction with an evaluation procedure
608. Depending upon implementation details, the selective
correction procedure 610 may additionally involve selectively
avoiding image resampling or correction. Exemplary evaluation 608
and adaptive registration 600 procedures are further described
below. Following the selective correction procedure 610, the
adaptive registration procedure 600 may also include an adjustment
procedure 612 that involves performing filtering and/or other image
processing operations. These adjustment procedures may include, by
way of example, noise reduction, contrast enhancement, and window
and level procedures. Such adjustment procedures are well known in
the art and need not be described herein.
[0050] FIG. 7 is a flowchart of exemplary evaluation 608 and
selective correction 610 procedures according to an embodiment of
the invention. Relative to FIG. 6, like reference numbers indicate
like procedures, and the z axis corresponds to a lowest resolution
dimension. In one embodiment, the evaluation procedure 608
determines whether estimated motion along the z axis equals,
approximately equals, or exceeds a correction threshold, for
example, 0.5 times z axis resolution. Thus, if resolution along z
equals 1.5 mm, the evaluation procedure 608 may determine whether
estimated motion along z exceeds 0.75 mm (corresponding to a
situation in which .alpha. equals 0.5). If so, the selective
correction procedure 610 may initiate and/or perform a 3D rigid or
nonrigid correction; otherwise, the selective correction procedure
610 may initiate and/or perform a 2D rigid or nonrigid correction.
A 3D correction may provide greater accuracy than a 2D correction,
but will generally require significantly more computational time
than a 2D correction. Thus, unless patient or tissue movement is
significant relative to resolution, a 3D correction may be
unnecessary, and particular embodiments of the invention may save
time by performing a 2D correction. Performance of a rigid or
nonrigid correction may be dependent upon a type of motion model
and/or motion estimation technique employed during the motion
estimation procedure 606, clinical conditions, tissue types under
consideration, available computational resources, and/or
computation time goals or constraints.
[0051] FIG. 8 is a flowchart of exemplary evaluation 608 and
selective correction 610 procedures according to another embodiment
of the invention. Relative to FIG. 6, like reference numbers
indicate like procedures, and the z axis corresponds to a lowest
resolution dimension. In one embodiment, the evaluation procedure
608 may determine whether estimated motion along the z axis is less
than a first z axis correction threshold, for example, 0.1 times z
axis resolution. If so, image resampling or correction is avoided.
Otherwise, the evaluation procedure 608 may determine whether
estimated motion along the z axis equals, approximately equals, or
exceeds a second z axis correction threshold, for example, 0.5
times z axis resolution. If so, the selective correction procedure
610 may initiate and/or perform a 3D rigid or nonrigid correction.
Otherwise, the evaluation procedure 608 may determine whether
estimated motion along an x or y axis equals, approximately equals,
or exceeds a corresponding x axis or y axis correction threshold.
In one embodiment, an x or y axis correction threshold may be, for
example, 0.8 times x or y resolution, respectively. If estimated
motion along an x or y axis meets the aforementioned conditions,
the selective correction procedure 610 may initiate and/or perform
a 3D rigid or nonrigid correction. Otherwise, the selective
correction procedure 610 may initiate or perform a 2D rigid or
nonrigid correction. Performance of a rigid or nonrigid correction
may be dependent upon a type of motion model and/or motion
estimation technique employed, and/or computational resources.
[0052] FIG. 9 is a block diagram of a system 900 for adaptive
medical image registration according to an embodiment of the
invention. The system 900 may comprise a medical imaging system
910, at least one data storage unit 920, and an adaptive
registration computer 940. In one embodiment, each element 910,
920, 940 is coupled to a computer network 990. The medical imaging
system 910 may comprise an MRI or other type of imaging system. The
data storage unit 920 may comprise one or more hard disk drives,
and may possibly comprise a Network Attached Storage (NAS) device.
The data storage unit 920 may receive, store, and/or transfer
imaging data as well as other information.
[0053] The adaptive registration computer 940 may comprise one or
more portions of a medical image analysis platform. The adaptive
registration computer 940 may include a processing unit and a
memory, and may further include one or more of a disk drive and/or
other data storage devices (e.g., optical and/or magnetooptical
data storage devices, tape drives, flash memory based drives,
etc.), an input device, and an output device. The memory, the disk
drive, and/or other data storage devices may comprise one or more
portions of computer readable media that store program instructions
and possibly data for performing one or more adaptive medical image
registration procedures and/or operations associated therewith in
accordance with particular embodiments of the invention. Depending
upon implementation details, the network 990 may comprise one or
more local or private networks such as a Local Area Network (LAN)
and/or one or more public networks such as the Internet. In an
alternate embodiment, the medical imaging system 910 and the
adaptive registration computer 940 may each have a separate data
storage unit 920, and imaging data and/or other information stored
upon removable media may be manually transferred between such data
storage units 920.
[0054] From the foregoing, it will be appreciated that specific
embodiments of the invention have been described herein for
purposes of illustration, but that various modifications may be
made without deviating from the spirit and scope of the invention.
Accordingly, the invention is not limited except as by the appended
claims.
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